The Next Input updates
Browse every published The Next Input update in a calm card overview with images, dates, and direct access to each article.
The Next Input update
Jukebox
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Improving verifiability in AI development
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OpenAI Microscope
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OpenAI standardizes on PyTorch
We are standardizing OpenAI’s deep learning framework on PyTorch.
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Scaling laws for neural language models
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Dota 2 with large scale deep reinforcement learning
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Deep double descent
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Procgen Benchmark
Procgen Benchmark consists of 16 unique environments designed to measure both sample efficiency and generalization in reinforcement learning. This benchmark is ideal for evaluating generalization since distinct training and test sets can be generated in each environment. This benchmark is also well-suited to evaluate sample efficiency, since all environments pose diverse and compelling challenges for RL agents. The environments’ intrinsic diversity demands that agents learn robust policies; overfitting to narrow regions in state space will not suffice. Put differently, the ability to generalize becomes an integral component of success when agents are faced with ever-changing levels.
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Safety Gym
To study constrained RL for safe exploration, we developed a new set of environments and tools called Safety Gym. By comparison to existing environments for constrained RL, Safety Gym environments are richer and feature a wider range of difficulty and complexity.
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Benchmarking safe exploration in deep reinforcement learning
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GPT-2: 1.5B release
Title: GPT-2: 1.5B release
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Solving Rubik’s Cube with a robot hand
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Showing 925 to 936 of 1,127 updates.